Our
<term>
resource-frugal approach
</term>
results in 87.5 %
<term>
agreement
</term>
with a state of the art , proprietary
<term>
Arabic stemmer
</term>
built using
<term>
rules
</term>
,
<term>
affix lists
</term>
, and
<term>
human annotated text
</term>
, in addition to an
<term>
unsupervised component
</term>
.
<term>
Task-based evaluation
</term>
using
<term>
Arabic information retrieval
</term>
indicates an improvement of 22-38 % in
<term>
average precision
</term>
over
<term>
unstemmed text
</term>
, and 96 % of the performance of the proprietary
<term>
stemmer
</term>
above .
#4571Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component. Task-based evaluation using Arabic information retrieval indicates an improvement of 22-38% in average precision over unstemmed text, and 96% of the performance of the proprietary stemmer above.
tech,34-6-P03-1050,ak
Our
<term>
resource-frugal approach
</term>
results in 87.5 %
<term>
agreement
</term>
with a state of the art , proprietary
<term>
Arabic stemmer
</term>
built using
<term>
rules
</term>
,
<term>
affix lists
</term>
, and
<term>
human annotated text
</term>
, in addition to an
<term>
unsupervised component
</term>
.
#4568Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component .
tech,1-6-P03-1050,ak
Our
<term>
resource-frugal approach
</term>
results in 87.5 %
<term>
agreement
</term>
with a state of the art , proprietary
<term>
Arabic stemmer
</term>
built using
<term>
rules
</term>
,
<term>
affix lists
</term>
, and
<term>
human annotated text
</term>
, in addition to an
<term>
unsupervised component
</term>
.
#4535Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component.
other,16-7-P03-1050,ak
<term>
Task-based evaluation
</term>
using
<term>
Arabic information retrieval
</term>
indicates an improvement of 22-38 % in
<term>
average precision
</term>
over
<term>
unstemmed text
</term>
, and 96 % of the performance of the proprietary
<term>
stemmer
</term>
above .
#4587Task-based evaluation using Arabic information retrieval indicates an improvement of 22-38% in average precision over unstemmed text , and 96% of the performance of the proprietary stemmer above.
tech,13-2-P03-1050,ak
The
<term>
stemming model
</term>
is based on
<term>
statistical machine translation
</term>
and it uses an
<term>
English stemmer
</term>
and a
<term>
small ( 10K sentences ) parallel corpus
</term>
as its sole
<term>
training resources
</term>
.
#4461The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10K sentences) parallel corpus as its sole training resources.
measure(ment),13-7-P03-1050,ak
<term>
Task-based evaluation
</term>
using
<term>
Arabic information retrieval
</term>
indicates an improvement of 22-38 % in
<term>
average precision
</term>
over
<term>
unstemmed text
</term>
, and 96 % of the performance of the proprietary
<term>
stemmer
</term>
above .
#4584Task-based evaluation using Arabic information retrieval indicates an improvement of 22-38% in average precision over unstemmed text, and 96% of the performance of the proprietary stemmer above.
tech,16-6-P03-1050,ak
Our
<term>
resource-frugal approach
</term>
results in 87.5 %
<term>
agreement
</term>
with a state of the art , proprietary
<term>
Arabic stemmer
</term>
built using
<term>
rules
</term>
,
<term>
affix lists
</term>
, and
<term>
human annotated text
</term>
, in addition to an
<term>
unsupervised component
</term>
.
#4550Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component.